我在Amazon Arora Postgres中有一个表。我需要将该表以csv格式移动到S3存储桶。我已经在AWS胶水中创建了以下pyspark代码。而不是将其作为csv文件存储在S3存储桶中。在S3存储桶中会创建多个文件，例如run-XXX-part1。有没有一种方法可以将rds表导出到S3中的csv文件中。码：导入系统从awsglue.transforms导入*从awsglue.utils导入getResolvedOptions从pyspark.context导入SparkContext从awsglue.context导入GlueContext从awsglue.job导入作业
## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
## @type: DataSource
## @args: [database = "test1", table_name = "testdb_public_reports3", transformation_ctx = "datasource0"]
## @return: datasource0
## @inputs: []
## @type: ApplyMapping
## @args: [mapping = [("orderapprovedby", "string", "orderapprovedby", "string"), ("lname", "string", "lname", "string"), ("unitofmeasurement", "string", "unitofmeasurement", "string"), ("orderrequesteddtm", "timestamp", "orderrequesteddtm", "timestamp"), ("orderdeliverydtm", "timestamp", "orderdeliverydtm", "timestamp"), ("allowedqty", "decimal(10,2)", "allowedqty", "decimal(10,2)"), ("addressid", "int", "addressid", "int"), ("fname", "string", "fname", "string")], transformation_ctx = "applymapping1"]
## @return: applymapping1
## @inputs: [frame = datasource0]
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("mname", "string", "mname", "string"), ("lname", "string", "lname", "string"), ("designation", "string", "designation", "string"), ("joiningtime", "timestamp", "joiningtime", "timestamp"), ("leavingtime", "timestamp", "orderdeliverydtm", "leavingtime"),("fname", "string", "fname", "string")], transformation_ctx = "applymapping1")
## @type: DataSink
## @args: [connection_type = "s3", connection_options = {"path": "s3://deloitte-homefront-poc/PROCESSED"}, format = "csv", transformation_ctx = "datasink2"]
## @return: datasink2
## @inputs: [frame = applymapping1]
datasink2 = glueContext.write_dynamic_frame.from_options(frame = applymapping1, connection_type = "s3", connection_options = {"path": "s3://path"}, format = "csv", transformation_ctx = "datasink2")
job.commit()
仅将胶水和pyspark用于导出数据不是一个好的选择。您可以按照AWS提供的分步指南进行操作https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/postgresql-s3-export.html
您仍然想要使用Glue并想要单个输出文件
#replace
datasink2 = glueContext.write_dynamic_frame.from_options(frame = applymapping1, connection_type = "s3", connection_options = {"path": "s3://path"}, format = "csv", transformation_ctx = "datasink2")
#with
df=applymapping1.toDF()
df.repartition(1).write.csv(path)